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Tractable approximate robust geometric programming
 OPTIM ENG
, 2006
"... The optimal solution of a geometric program (GP) can be sensitive to variations in the problem data. Robust geometric programming can systematically alleviate the sensitivity problem by explicitly incorporating a model of data uncertainty in a GP and optimizing for the worstcase scenario under th ..."
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Cited by 13 (1 self)
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this model. However, it is not known whether a general robust GP can be reformulated as a tractable optimization problem that interiorpoint or other algorithms can efficiently solve. In this paper we propose an approximation method that seeks a compromise between solution accuracy and computational
Tractable Approximations for Temporal Constraint Handling
 Artificial Intelligence
, 1999
"... Relation algebras have been used for various kinds of temporal reasoning. Typically the network satisfaction problem turns out to be NPhard. For the Allen interval algebra it is often convenient to use the propagation algorithm. This algorithm is sound and runs in cubic time but it is not compl ..."
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Cited by 4 (1 self)
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but it is not complete. Here we define a series of tractable algorithms that provide approximations to solving the network satisfaction problem for any finite relation algebra. For algebras where all 3consistent atomic networks are satisfiable, like the Allen interval algebra, we can improve these algorithms so
TRACTABLE APPROXIMATIONS OF SETS DEFINED WITH QUANTIFIERS
"... Abstract. Given a compact basic semialgebraic set K ⊂ R n × R m, a simple set B (box or ellipsoid), and some semialgebraic function f, we consider sets defined with quantifiers, of the form Rf: = {x ∈ B: f(x, y) ≤ 0 for all y such that (x, y) ∈ K} Df: = {x ∈ B: f(x, y) ≤ 0 for some y such that ..."
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Cited by 1 (0 self)
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) ≥ 0}. We provide a systematic procedure to obtain a sequence of explicit inner (resp. outer) approximations that converge to Rf (resp. Df) in a strong sense. An additional feature is that each approximation is the sublevel set of a single polynomial whose vector of coefficients is an optimal solution
Tractable approximations of robust conic optimization problems
, 2006
"... In earlier proposals, the robust counterpart of conic optimization problems exhibits a lateral increase in complexity, i.e., robust linear programming problems (LPs) become second order cone problems (SOCPs), robust SOCPs become semidefinite programming problems (SDPs), and robust SDPs become NPha ..."
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Cited by 68 (15 self)
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hard. We propose a relaxed robust counterpart for general conic optimization problems that (a) preserves the computational tractability of the nominal problem; specifically the robust conic optimization problem retains its original structure, i.e., robust LPs remain LPs, robust SOCPs remain SOCPs
A Proof Theory for Tractable Approximations of Propositional Reasoning
 Proceedings of the Fifth Conference of the Italian Association for Artificial Intelligence (AI*IA97), volume 1321 of Lecture Notes In Artificial Intelligence
, 1997
"... . This paper proposes an uniform framework for the proof theory of tractable approximations of propositional reasoning. The key idea is the introduction of approximate proofs. This makes possible the development of an approximating sequent calculus for propositional deduction where proofs can be sou ..."
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Cited by 2 (1 self)
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. This paper proposes an uniform framework for the proof theory of tractable approximations of propositional reasoning. The key idea is the introduction of approximate proofs. This makes possible the development of an approximating sequent calculus for propositional deduction where proofs can
Distributionally robust optimization and its tractable approximations
 Operations Research
"... In this paper, we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust, and more flexible than the standard techni ..."
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Cited by 25 (4 self)
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In this paper, we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust, and more flexible than the standard
Tractable Approximate Deduction using Limited Vocabularies
 IN PROCEEDINGS OF CSCSI92
, 1992
"... A new approach to tractable deduction from an expressive knowledge base is presented that approximates formulae by automatically mapping them to some restricted language. Various mappings and their properties are discussed, and an anytime algorithm to compute approximations is presented. Several pub ..."
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Cited by 17 (1 self)
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A new approach to tractable deduction from an expressive knowledge base is presented that approximates formulae by automatically mapping them to some restricted language. Various mappings and their properties are discussed, and an anytime algorithm to compute approximations is presented. Several
Tractable Approximation to Optimal Planning in the Blocks World
, 1995
"... This paper is about planning in one of the simplest domains: the Blocks World (BW). In the first part, we examine some known polynomial time algorithms for BW planning which approximate optimality. We improve the known complexity bounds of two such algorithms, and give the first proper formulatio ..."
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This paper is about planning in one of the simplest domains: the Blocks World (BW). In the first part, we examine some known polynomial time algorithms for BW planning which approximate optimality. We improve the known complexity bounds of two such algorithms, and give the first proper
1Computationallytractable approximate PHD and CPHD filters for superpositional sensors
"... Abstract—In this paper we derive computationallytractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle ..."
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Abstract—In this paper we derive computationallytractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary
Results 1  10
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